/*
 * $Id: maxent.cpp,v 1.1.1.1 2007/05/15 08:30:35 kyoshida Exp $
 */

#include "maxent.h"
#include <cmath>
#include <cstdio>
#include "lbfgs.h"

using namespace std;

double ME_Model::FunctionGradient(const vector<double>& x,
                                  vector<double>& grad) {
  assert((int)_fb.Size() == x.size());
  for (size_t i = 0; i < x.size(); i++) {
    _vl[i] = x[i];
  }

  double score = update_model_expectation();

  if (_l2reg == 0) {
    for (size_t i = 0; i < x.size(); i++) {
      grad[i] = -(_vee[i] - _vme[i]);
    }
  } else {
    const double c = _l2reg * 2;
    for (size_t i = 0; i < x.size(); i++) {
      grad[i] = -(_vee[i] - _vme[i] - c * _vl[i]);
    }
  }

  return -score;
}

int ME_Model::perform_GIS(int C) {
  cerr << "C = " << C << endl;
  C = 1;
  cerr << "performing AGIS" << endl;
  vector<double> pre_v;
  double pre_logl = -999999;
  for (int iter = 0; iter < 200; iter++) {

    double logl = update_model_expectation();
    fprintf(stderr, "iter = %2d  C = %d  f = %10.7f  train_err = %7.5f", iter,
            C, logl, _train_error);
    if (_heldout.size() > 0) {
      double hlogl = heldout_likelihood();
      fprintf(stderr, "  heldout_logl(err) = %f (%6.4f)", hlogl,
              _heldout_error);
    }
    cerr << endl;

    if (logl < pre_logl) {
      C += 1;
      _vl = pre_v;
      iter--;
      continue;
    }
    if (C > 1 && iter % 10 == 0) C--;

    pre_logl = logl;
    pre_v = _vl;
    for (int i = 0; i < _fb.Size(); i++) {
      double coef = _vee[i] / _vme[i];
      _vl[i] += log(coef) / C;
    }
  }
  cerr << endl;

  return 0;
}

int ME_Model::perform_QUASI_NEWTON() {
  const int dim = _fb.Size();
  vector<double> x0(dim);

  for (int i = 0; i < dim; i++) {
    x0[i] = _vl[i];
  }

  vector<double> x;
  if (_l1reg > 0) {
    cerr << "performing OWLQN" << endl;
    x = perform_OWLQN(x0, _l1reg);
  } else {
    cerr << "performing LBFGS" << endl;
    x = perform_LBFGS(x0);
  }

  for (int i = 0; i < dim; i++) {
    _vl[i] = x[i];
  }

  return 0;
}

int ME_Model::conditional_probability(const Sample& s,
                                      std::vector<double>& membp) const {
  // int num_classes = membp.size();
  double sum = 0;
  int max_label = -1;
  //  double maxp = 0;

  vector<double> powv(_num_classes, 0.0);
  for (vector<int>::const_iterator j = s.positive_features.begin();
       j != s.positive_features.end(); j++) {
    for (vector<int>::const_iterator k = _feature2mef[*j].begin();
         k != _feature2mef[*j].end(); k++) {
      powv[_fb.Feature(*k).label()] += _vl[*k];
    }
  }
  for (vector<pair<int, double> >::const_iterator j = s.rvfeatures.begin();
       j != s.rvfeatures.end(); j++) {
    for (vector<int>::const_iterator k = _feature2mef[j->first].begin();
         k != _feature2mef[j->first].end(); k++) {
      powv[_fb.Feature(*k).label()] += _vl[*k] * j->second;
    }
  }

  std::vector<double>::const_iterator pmax =
      max_element(powv.begin(), powv.end());
  double offset = max(0.0, *pmax - 700);  // to avoid overflow
  for (int label = 0; label < _num_classes; label++) {
    double pow = powv[label] - offset;
    double prod = exp(pow);
    //      cout << pow << " " << prod << ", ";
    //      if (_ref_modelp != NULL) prod *= _train_refpd[n][label];
    if (_ref_modelp != NULL) prod *= s.ref_pd[label];
    assert(prod != 0);
    membp[label] = prod;
    sum += prod;
  }
  for (int label = 0; label < _num_classes; label++) {
    membp[label] /= sum;
    if (membp[label] > membp[max_label]) max_label = label;
  }
  assert(max_label >= 0);
  return max_label;
}

int ME_Model::make_feature_bag(const int cutoff) {
  int max_num_features = 0;

// count the occurrences of features
#ifdef USE_HASH_MAP
  typedef __gnu_cxx::hash_map<unsigned int, int> map_type;
#else
  typedef std::map<unsigned int, int> map_type;
#endif
  map_type count;
  if (cutoff > 0) {
    for (std::vector<Sample>::const_iterator i = _vs.begin(); i != _vs.end();
         i++) {
      for (std::vector<int>::const_iterator j = i->positive_features.begin();
           j != i->positive_features.end(); j++) {
        count[ME_Feature(i->label, *j).body()]++;
      }
      for (std::vector<pair<int, double> >::const_iterator j =
               i->rvfeatures.begin();
           j != i->rvfeatures.end(); j++) {
        count[ME_Feature(i->label, j->first).body()]++;
      }
    }
  }

  int n = 0;
  for (std::vector<Sample>::const_iterator i = _vs.begin(); i != _vs.end();
       i++, n++) {
    max_num_features =
        max(max_num_features, (int)(i->positive_features.size()));
    for (std::vector<int>::const_iterator j = i->positive_features.begin();
         j != i->positive_features.end(); j++) {
      const ME_Feature feature(i->label, *j);
      //      if (cutoff > 0 && count[feature.body()] < cutoff) continue;
      if (cutoff > 0 && count[feature.body()] <= cutoff) continue;
      _fb.Put(feature);
      //      cout << i->label << "\t" << *j << "\t" << id << endl;
      //      feature2sample[id].push_back(n);
    }
    for (std::vector<pair<int, double> >::const_iterator j =
             i->rvfeatures.begin();
         j != i->rvfeatures.end(); j++) {
      const ME_Feature feature(i->label, j->first);
      //      if (cutoff > 0 && count[feature.body()] < cutoff) continue;
      if (cutoff > 0 && count[feature.body()] <= cutoff) continue;
      _fb.Put(feature);
    }
  }
  count.clear();

  //  cerr << "num_classes = " << _num_classes << endl;
  //  cerr << "max_num_features = " << max_num_features << endl;

  init_feature2mef();

  return max_num_features;
}

double ME_Model::heldout_likelihood() {
  double logl = 0;
  int ncorrect = 0;
  for (std::vector<Sample>::const_iterator i = _heldout.begin();
       i != _heldout.end(); i++) {
    vector<double> membp(_num_classes);
    int l = classify(*i, membp);
    logl += log(membp[i->label]);
    if (l == i->label) ncorrect++;
  }
  _heldout_error = 1 - (double)ncorrect / _heldout.size();

  return logl /= _heldout.size();
}

double ME_Model::update_model_expectation() {
  double logl = 0;
  int ncorrect = 0;

  _vme.resize(_fb.Size());
  for (int i = 0; i < _fb.Size(); i++) _vme[i] = 0;

  int n = 0;
  for (vector<Sample>::const_iterator i = _vs.begin(); i != _vs.end();
       i++, n++) {
    vector<double> membp(_num_classes);
    int max_label = conditional_probability(*i, membp);

    logl += log(membp[i->label]);
    //    cout << membp[*i] << " " << logl << " ";
    if (max_label == i->label) ncorrect++;

    // model_expectation
    for (vector<int>::const_iterator j = i->positive_features.begin();
         j != i->positive_features.end(); j++) {
      for (vector<int>::const_iterator k = _feature2mef[*j].begin();
           k != _feature2mef[*j].end(); k++) {
        _vme[*k] += membp[_fb.Feature(*k).label()];
      }
    }
    for (vector<pair<int, double> >::const_iterator j = i->rvfeatures.begin();
         j != i->rvfeatures.end(); j++) {
      for (vector<int>::const_iterator k = _feature2mef[j->first].begin();
           k != _feature2mef[j->first].end(); k++) {
        _vme[*k] += membp[_fb.Feature(*k).label()] * j->second;
      }
    }
  }

  for (int i = 0; i < _fb.Size(); i++) {
    _vme[i] /= _vs.size();
  }

  _train_error = 1 - (double)ncorrect / _vs.size();

  logl /= _vs.size();

  if (_l2reg > 0) {
    const double c = _l2reg;
    for (int i = 0; i < _fb.Size(); i++) {
      logl -= _vl[i] * _vl[i] * c;
    }
  }

  // logl /= _vs.size();

  //  fprintf(stderr, "iter =%3d  logl = %10.7f  train_acc = %7.5f\n", iter,
  // logl, (double)ncorrect/train.size());
  //  fprintf(stderr, "logl = %10.7f  train_acc = %7.5f\n", logl,
  // (double)ncorrect/_train.size());

  return logl;
}

int ME_Model::train(const vector<ME_Sample>& vms) {
  _vs.clear();
  for (vector<ME_Sample>::const_iterator i = vms.begin(); i != vms.end(); i++) {
    add_training_sample(*i);
  }

  return train();
}

void ME_Model::add_training_sample(const ME_Sample& mes) {
  Sample s;
  s.label = _label_bag.Put(mes.label);
  if (s.label > ME_Feature::MAX_LABEL_TYPES) {
    cerr << "error: too many types of labels." << endl;
    exit(1);
  }
  for (vector<string>::const_iterator j = mes.features.begin();
       j != mes.features.end(); j++) {
    s.positive_features.push_back(_featurename_bag.Put(*j));
  }
  for (vector<pair<string, double> >::const_iterator j = mes.rvfeatures.begin();
       j != mes.rvfeatures.end(); j++) {
    s.rvfeatures.push_back(
        pair<int, double>(_featurename_bag.Put(j->first), j->second));
  }
  if (_ref_modelp != NULL) {
    ME_Sample tmp = mes;
    ;
    s.ref_pd = _ref_modelp->classify(tmp);
  }
  //  cout << s.label << "\t";
  //  for (vector<int>::const_iterator j = s.positive_features.begin(); j !=
  // s.positive_features.end(); j++){
  //    cout << *j << " ";
  //  }
  //  cout << endl;

  _vs.push_back(s);
}

int ME_Model::train() {
  if (_l1reg > 0 && _l2reg > 0) {
    cerr << "error: L1 and L2 regularizers cannot be used simultaneously."
         << endl;
    return 0;
  }
  if (_vs.size() == 0) {
    cerr << "error: no training data." << endl;
    return 0;
  }
  if (_nheldout >= (int)_vs.size()) {
    cerr << "error: too much heldout data. no training data is available."
         << endl;
    return 0;
  }
  //  if (_nheldout > 0) random_shuffle(_vs.begin(), _vs.end());

  int max_label = 0;
  for (std::vector<Sample>::const_iterator i = _vs.begin(); i != _vs.end();
       i++) {
    max_label = max(max_label, i->label);
  }
  _num_classes = max_label + 1;
  if (_num_classes != _label_bag.Size()) {
    cerr << "warning: _num_class != _label_bag.Size()" << endl;
  }

  if (_ref_modelp != NULL) {
    cerr << "setting reference distribution...";
    for (int i = 0; i < _ref_modelp->num_classes(); i++) {
      _label_bag.Put(_ref_modelp->get_class_label(i));
    }
    _num_classes = _label_bag.Size();
    for (vector<Sample>::iterator i = _vs.begin(); i != _vs.end(); i++) {
      set_ref_dist(*i);
    }
    cerr << "done" << endl;
  }

  for (int i = 0; i < _nheldout; i++) {
    _heldout.push_back(_vs.back());
    _vs.pop_back();
  }

  sort(_vs.begin(), _vs.end());

  int cutoff = 0;
  if (cutoff > 0) cerr << "cutoff threshold = " << cutoff << endl;
  if (_l1reg > 0) cerr << "L1 regularizer = " << _l1reg << endl;
  if (_l2reg > 0) cerr << "L2 regularizer = " << _l2reg << endl;

  // normalize
  _l1reg /= _vs.size();
  _l2reg /= _vs.size();

  cerr << "preparing for estimation...";
  make_feature_bag(cutoff);
  //  _vs.clear();
  cerr << "done" << endl;
  cerr << "number of samples = " << _vs.size() << endl;
  cerr << "number of features = " << _fb.Size() << endl;

  cerr << "calculating empirical expectation...";
  _vee.resize(_fb.Size());
  for (int i = 0; i < _fb.Size(); i++) {
    _vee[i] = 0;
  }
  for (int n = 0; n < (int)_vs.size(); n++) {
    const Sample* i = &_vs[n];
    for (vector<int>::const_iterator j = i->positive_features.begin();
         j != i->positive_features.end(); j++) {
      for (vector<int>::const_iterator k = _feature2mef[*j].begin();
           k != _feature2mef[*j].end(); k++) {
        if (_fb.Feature(*k).label() == i->label) _vee[*k] += 1.0;
      }
    }

    for (vector<pair<int, double> >::const_iterator j = i->rvfeatures.begin();
         j != i->rvfeatures.end(); j++) {
      for (vector<int>::const_iterator k = _feature2mef[j->first].begin();
           k != _feature2mef[j->first].end(); k++) {
        if (_fb.Feature(*k).label() == i->label) _vee[*k] += j->second;
      }
    }
  }
  for (int i = 0; i < _fb.Size(); i++) {
    _vee[i] /= _vs.size();
  }
  cerr << "done" << endl;

  _vl.resize(_fb.Size());
  for (int i = 0; i < _fb.Size(); i++) _vl[i] = 0.0;

  if (_optimization_method == SGD) {
    perform_SGD();
  } else {
    perform_QUASI_NEWTON();
  }

  int num_active = 0;
  for (int i = 0; i < _fb.Size(); i++) {
    if (_vl[i] != 0) num_active++;
  }
  cerr << "number of active features = " << num_active << endl;

  return 0;
}

void ME_Model::get_features(list<pair<pair<string, string>, double> >& fl) {
  fl.clear();
  //  for (int i = 0; i < _fb.Size(); i++) {
  //    ME_Feature f = _fb.Feature(i);
  //    fl.push_back( make_pair(make_pair(_label_bag.Str(f.label()),
  // _featurename_bag.Str(f.feature())), _vl[i]));
  //  }
  for (MiniStringBag::map_type::const_iterator i = _featurename_bag.begin();
       i != _featurename_bag.end(); i++) {
    for (int j = 0; j < _label_bag.Size(); j++) {
      string label = _label_bag.Str(j);
      string history = i->first;
      int id = _fb.Id(ME_Feature(j, i->second));
      if (id < 0) continue;
      fl.push_back(make_pair(make_pair(label, history), _vl[id]));
    }
  }
}

void ME_Model::clear() {
  _vl.clear();
  _label_bag.Clear();
  _featurename_bag.Clear();
  _fb.Clear();
  _feature2mef.clear();
  _vee.clear();
  _vme.clear();
  _vs.clear();
  _heldout.clear();
}

bool ME_Model::load_from_file(const string& filename) {
  FILE* fp = fopen(filename.c_str(), "r");
  if (!fp) {
    cerr << "error: cannot open " << filename << "!" << endl;
    return false;
  }

  _vl.clear();
  _label_bag.Clear();
  _featurename_bag.Clear();
  _fb.Clear();
  char buf[1024];
  while (fgets(buf, 1024, fp)) {
    string line(buf);
    string::size_type t1 = line.find_first_of('\t');
    string::size_type t2 = line.find_last_of('\t');
    string classname = line.substr(0, t1);
    string featurename = line.substr(t1 + 1, t2 - (t1 + 1));
    float lambda;
    string w = line.substr(t2 + 1);
    sscanf(w.c_str(), "%f", &lambda);

    int label = _label_bag.Put(classname);
    int feature = _featurename_bag.Put(featurename);
    _fb.Put(ME_Feature(label, feature));
    _vl.push_back(lambda);
  }

  _num_classes = _label_bag.Size();

  init_feature2mef();

  fclose(fp);

  return true;
}

void ME_Model::init_feature2mef() {
  _feature2mef.clear();
  for (int i = 0; i < _featurename_bag.Size(); i++) {
    vector<int> vi;
    for (int k = 0; k < _num_classes; k++) {
      int id = _fb.Id(ME_Feature(k, i));
      if (id >= 0) vi.push_back(id);
    }
    _feature2mef.push_back(vi);
  }
}

bool ME_Model::load_from_array(const ME_Model_Data data[]) {
  _vl.clear();
  for (int i = 0;; i++) {
    if (string(data[i].label) == "///") break;
    int label = _label_bag.Put(data[i].label);
    int feature = _featurename_bag.Put(data[i].feature);
    _fb.Put(ME_Feature(label, feature));
    _vl.push_back(data[i].weight);
  }
  _num_classes = _label_bag.Size();

  init_feature2mef();

  return true;
}

bool ME_Model::save_to_file(const string& filename, const double th) const {
  FILE* fp = fopen(filename.c_str(), "w");
  if (!fp) {
    cerr << "error: cannot open " << filename << "!" << endl;
    return false;
  }

  //  for (int i = 0; i < _fb.Size(); i++) {
  //    if (_vl[i] == 0) continue; // ignore zero-weight features
  //    ME_Feature f = _fb.Feature(i);
  //    fprintf(fp, "%s\t%s\t%f\n", _label_bag.Str(f.label()).c_str(),
  // _featurename_bag.Str(f.feature()).c_str(), _vl[i]);
  //  }
  for (MiniStringBag::map_type::const_iterator i = _featurename_bag.begin();
       i != _featurename_bag.end(); i++) {
    for (int j = 0; j < _label_bag.Size(); j++) {
      string label = _label_bag.Str(j);
      string history = i->first;
      int id = _fb.Id(ME_Feature(j, i->second));
      if (id < 0) continue;
      if (_vl[id] == 0) continue;        // ignore zero-weight features
      if (fabs(_vl[id]) < th) continue;  // cut off low-weight features
      fprintf(fp, "%s\t%s\t%f\n", label.c_str(), history.c_str(), _vl[id]);
    }
  }

  fclose(fp);

  return true;
}

void ME_Model::set_ref_dist(Sample& s) const {
  vector<double> v0 = s.ref_pd;
  vector<double> v(_num_classes);
  for (unsigned int i = 0; i < v.size(); i++) {
    v[i] = 0;
    string label = get_class_label(i);
    int id_ref = _ref_modelp->get_class_id(label);
    if (id_ref != -1) {
      v[i] = v0[id_ref];
    }
    if (v[i] == 0) v[i] = 0.001;  // to avoid -inf logl
  }
  s.ref_pd = v;
}

int ME_Model::classify(const Sample& nbs, vector<double>& membp) const {
  //  vector<double> membp(_num_classes);
  assert(_num_classes == (int)membp.size());
  conditional_probability(nbs, membp);
  int max_label = 0;
  double max = 0.0;
  for (int i = 0; i < (int)membp.size(); i++) {
    //    cout << membp[i] << " ";
    if (membp[i] > max) {
      max_label = i;
      max = membp[i];
    }
  }
  //  cout << endl;
  return max_label;
}

vector<double> ME_Model::classify(ME_Sample& mes) const {
  Sample s;
  for (vector<string>::const_iterator j = mes.features.begin();
       j != mes.features.end(); j++) {
    int id = _featurename_bag.Id(*j);
    if (id >= 0) s.positive_features.push_back(id);
  }
  for (vector<pair<string, double> >::const_iterator j = mes.rvfeatures.begin();
       j != mes.rvfeatures.end(); j++) {
    int id = _featurename_bag.Id(j->first);
    if (id >= 0) {
      s.rvfeatures.push_back(pair<int, double>(id, j->second));
    }
  }
  if (_ref_modelp != NULL) {
    s.ref_pd = _ref_modelp->classify(mes);
    set_ref_dist(s);
  }

  vector<double> vp(_num_classes);
  int label = classify(s, vp);
  mes.label = get_class_label(label);
  return vp;
}

/*
 * $Log: maxent.cpp,v $
 * Revision 1.1.1.1  2007/05/15 08:30:35  kyoshida
 * stepp tagger, by Okanohara and Tsuruoka
 *
 * Revision 1.28  2006/08/21 17:30:38  tsuruoka
 * use MAX_LABEL_TYPES
 *
 * Revision 1.27  2006/07/25 13:19:53  tsuruoka
 * sort _vs[]
 *
 * Revision 1.26  2006/07/18 11:13:15  tsuruoka
 * modify comments
 *
 * Revision 1.25  2006/07/18 10:02:15  tsuruoka
 * remove sample2feature[]
 * speed up conditional_probability()
 *
 * Revision 1.24  2006/07/18 05:10:51  tsuruoka
 * add ref_dist
 *
 * Revision 1.23  2005/12/24 07:05:32  tsuruoka
 * modify conditional_probability() to avoid overflow
 *
 * Revision 1.22  2005/12/24 07:01:25  tsuruoka
 * add cutoff for real-valued features
 *
 * Revision 1.21  2005/12/23 10:33:02  tsuruoka
 * support real-valued features
 *
 * Revision 1.20  2005/12/23 09:15:29  tsuruoka
 * modify _train to reduce memory consumption
 *
 * Revision 1.19  2005/10/28 13:10:14  tsuruoka
 * fix for overflow (thanks to Ming Li)
 *
 * Revision 1.18  2005/10/28 13:03:07  tsuruoka
 * add progress_bar
 *
 * Revision 1.17  2005/09/12 13:51:16  tsuruoka
 * Sample: list -> vector
 *
 * Revision 1.16  2005/09/12 13:27:10  tsuruoka
 * add add_training_sample()
 *
 * Revision 1.15  2005/04/27 11:22:27  tsuruoka
 * bugfix
 * ME_Sample: list -> vector
 *
 * Revision 1.14  2005/04/27 10:00:42  tsuruoka
 * remove tmpfb
 *
 * Revision 1.13  2005/04/26 14:25:53  tsuruoka
 * add MiniStringBag, USE_HASH_MAP
 *
 * Revision 1.12  2005/02/11 10:20:08  tsuruoka
 * modify cutoff
 *
 * Revision 1.11  2004/10/04 05:50:25  tsuruoka
 * add Clear()
 *
 * Revision 1.10  2004/08/26 16:52:26  tsuruoka
 * fix load_from_file()
 *
 * Revision 1.9  2004/08/09 12:27:21  tsuruoka
 * change messages
 *
 * Revision 1.8  2004/08/04 13:55:18  tsuruoka
 * modify _sample2feature
 *
 * Revision 1.7  2004/07/28 13:42:58  tsuruoka
 * add AGIS
 *
 * Revision 1.6  2004/07/28 05:54:13  tsuruoka
 * get_class_name() -> get_class_label()
 * ME_Feature: bugfix
 *
 * Revision 1.5  2004/07/27 16:58:47  tsuruoka
 * modify the interface of classify()
 *
 * Revision 1.4  2004/07/26 17:23:46  tsuruoka
 * _sample2feature: list -> vector
 *
 * Revision 1.3  2004/07/26 15:49:23  tsuruoka
 * modify ME_Feature
 *
 * Revision 1.2  2004/07/26 13:52:18  tsuruoka
 * modify cutoff
 *
 * Revision 1.1  2004/07/26 13:10:55  tsuruoka
 * add files
 *
 * Revision 1.20  2004/07/22 08:34:45  tsuruoka
 * modify _sample2feature[]
 *
 * Revision 1.19  2004/07/21 16:33:01  tsuruoka
 * remove some comments
 *
 */